I spent long hours in the film room in college football, breaking down plays frame by frame. As a center, you’re responsible for the whole offensive line. You’re making protection calls, reading the defense, and communicating the threat before the snap. Coaches, playbooks, and footage of yourself and your opponents – every one of those inputs becomes part of your edge. You earned those insights the hard way, and the preparation you put in during the week becomes the team’s success.
It also stays with you. You don’t share it outside of the team.
The instinct to protect what gives you an advantage is exactly the right frame for understanding why so many sports organizations are stuck with AI right now. And it’s why what we proved with an NFL team at SHI AI Labs matters.
Through our Country Digital Acceleration (CDA) program, we deployed AI infrastructure designed to run advanced analytics entirely within the organization’s own environment. No data leaving the building, no black-box model, and no cloud dependency on systems that have no stake in whether the team wins on Sunday. The result wasn’t just a faster pipeline. It was something harder to build and more valuable to own: confidence. The confidence to actually act on what the data was telling them.
AI only becomes useful when it’s grounded in your data: your playbooks, your systems, your way of operating. Out-of-the-box models can’t deliver that. Without that grounding, you lose relevance and trust.
The problem isn’t the models. It’s everything around them.
Teams across the league are experimenting with AI. They have the data: player tracking, performance metrics, opponent tendencies, scouting reports, and fan behavior. They have access to powerful models. But most teams are still stuck in pilot mode.
Not because the technology isn’t capable enough. Because the environment around it isn’t built for what AI actually demands in a sports context.
Think about what’s at stake. Playbooks, contract details, and proprietary game strategy developed over months. Even new practice tape represents a genuine competitive edge, until they don’t. Sports organizations are working with some of the most sensitive, time-critical data of any industry. They operate on weekly cycles where a single leaked insight at the wrong moment can have real consequences.
In other parts of the ecosystem, like broadcasting and fan experiences, those constraints become even more immediate, where real-time delivery is critical. A delay of even a few seconds can break the experience, whether it’s live stats syncing with a broadcast, in-venue interactions, or second-screen engagement.
The standard enterprise AI playbook – move your data to a cloud environment, run models there, get insights back – creates a fundamental tension with that reality. You’re asking organizations to hand their most sensitive competitive assets to infrastructure they don’t fully control, in exchange for analytical power they could have kept in-house.
Most organizations sense this tension. Very few have resolved it.
That’s exactly where SūmerSports comes in. Their platform is purpose-built for sports, designed to ingest the unique data sets teams rely on, and apply models that are tuned to the realities of the game, from player performance to team strategy.
This is AI built for how sports teams actually operate.
Don’t move the data. Move the intelligence.
The approach we proved with that NFL team is conceptually simple: run the AI where the data already lives. Keep everything inside the organization’s own environment — governed, secured, fully under their control — and bring the intelligence to the data rather than the other way around.
That same philosophy applies to the infrastructure itself.
SūmerBrain, SūmerSports’ AI engine purpose-built for professional sports operations, now runs on Cisco AI PODs: pre-integrated, validated full-stack solutions designed to take the hardest part of AI off the table.
This is plug-and-play AI infrastructure: Easy to assemble, no hoping the components work together. It’s a system organizations can stand up quickly and trust to run consistently from day one.
That simplicity matters more than it sounds. Most teams don’t fail at AI because of the model. They get stuck because getting everything around the model to work together – compute, networking, security, data pipelines and operations – is too slow, too complex, and too risky.
In sports, the cost of getting stuck is measured in outcomes on the field. When you remove that friction, AI stops being a project—and starts becoming an operational advantage.
As CEO Lorrissa Horton recently posted: “Organizations don’t fail at AI because the model underperforms. They fail because everything around the model…is slower and harder than anyone planned.”
Together, SūmerBrain and Cisco AI PODs make it possible to go from data to insight much faster, without the integration work that typically slows teams down.
The Moneyball moment already happened. This is the next one.
The realization that better data could reshape competitive outcomes is old news now. Every organization accepts it. The question isn’t whether to use data anymore. It’s whether you can operationalize AI fast enough, and securely enough, to turn that data into a real edge before someone else does.
Coming from the college football world, I understand something about that pressure. The edge you’re chasing isn’t abstract. It’s the difference between a coaching staff that walks into Saturday with conviction and one that walks in with questions. AI should be building that conviction. It can’t do that if the people using it don’t fully trust where it’s running or what it’s doing with their data.
The same logic extends beyond football operations. In the front office, secure AI infrastructure changes what’s possible in draft strategy, roster construction, and contract decisions. This isn’t because the analysis gets better, but because executives can trust and act on it.
In the back office, the same rigor applied to the game can be applied to the business: translating fan behavior into personalized experiences, optimizing stadium operations, finding revenue opportunities and patterns that would be impossible to surface manually, simply because of the scale and complexity involved.
The organizations that pull ahead won’t be the ones with access to the best models. Everyone has good models now. The winners will be the ones that built the environment to actually use them — where the data is trusted, the infrastructure is reliable, and the gap between insight and decision is measured in seconds, not meetings.
That’s what we proved in a real environment, with a real NFL team, under real conditions.
The technology is ready. The question is whether your infrastructure is.
Explore Cisco AI PODs to learn how you can stand up your AI use cases and put secure, game-ready intelligence inside your organization today.
